The early detection of cervical cancer. The current and changing landscape of cervical disease detection
Bibliographic record
Abstract
Cervical cancer prevention has undergone dramatic changes over the past decade. With the introduction of human papillomavirus (HPV) vaccination, some countries have seen a dramatic decline in HPV-mediated cervical disease. However, widespread implementation has been limited by economic considerations and the varying healthcare priorities of different countries, as well as by vaccine availability and, in some instances, vaccine hesitancy amongst the population/government. In this environment, it is clear that cervical screening will retain a critical role in the prevention of cervical cancer and will in due course need to adapt to the changing incidence of HPV-associated neoplasia. Cervical screening has, for many years, been performed using Papanicolaou staining of cytology samples. As our understanding of the role of HPV in cervical cancer progression has advanced, and with the availability of sensitive detection systems, cervical screening now incorporates HPV testing. Although such tests improve disease detection, they are not specific, and cannot discriminate high-grade from low-grade disease. This has necessitated the development of effective triage approaches to stratify HPV-positive women according to their risk of cancer progression. Although cytology triage remains the mainstay of screening, novel strategies under evaluation include DNA methylation, biomarker detection and the incorporation of artificial intelligence systems to detect cervical abnormalities. These tests, which can be partially anchored in a molecular understanding of HPV pathogenesis, will enhance the sensitivity of disease detection and improve patient outcomes. This review will provide insight on these innovative methodologies while explaining their scientific basis drawing from our understanding of HPV tumour biology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".